1. Field of the Invention
The present invention relates to a robot apparatus having a plurality of movable sections including legs and a method of controlling the same. More particularly, the present invention relates to a robot apparatus adapted to control the motions of the movable sections thereof by means of oscillators having self-oscillation and entrainment characteristics and also to a method of controlling such a robot apparatus.
To be more specific, the present invention relates to a robot apparatus adapted to control the non-periodic (aperiodic) motions of the movable sections thereof by means of oscillators having entrainment characteristics and a method of controlling such a robot apparatus. More specifically, the present invention relates to a robot apparatus adapted to use a non-periodic signal as feedback signal to oscillators and having a large entrainment region and a method of controlling such a robot apparatus.
2. Description of the Related Art
Researches on and developments of structures of legged mobile robots and controlling such robots to make them walk stably have progressed remarkably in recent years and expectations are high for actual daily use of such robots. Legged mobile robots are instable if compared with crawler type robots and hence it is difficult to control the attitude and the locomotion of such robots. However, legged mobile robots are advantageous because of the capabilities of walking and running in a flexible manner as they can move up and down staircases and stride over obstacles.
Techniques for controlling the locomotion and other motions of legged mobile robots can be roughly divided into model-based approaches and non-model-based approaches.
Examples of model-based approaches include locomotion control techniques employing a Zero Moment Point (ZMP) as stability judging norm (see Jpn. Pat. Appln. Publication No. 3443077 and Jpn. Pat. Appln. Publication No. 3443116) and linear inverted pendulum control techniques (see S. Kajita, F. Kanehiro, K. Kaneko, K. Fujiwara, K. Yokoi and H. Hirukawa, “Biped walking pattern generation by a simple three-dimensional inverted pendulum model”, Advanced Robotics, vol. 17, No. 2, pp. 131-147, 2003). Model-based approaches are accompanied by problems including that the robot requires detailed model information including the center of gravity, the moment of inertia and the link length of each part of the robot to control motions of the robot, that the robot requires high precision actuators capable of accurately following joint angle trajectories designed based on model information at high manufacturing cost and that the robot becomes less robust when subjected to unexpected external force or formed to walk on an unleveled ground.
On the other hand, the bio-mechanisms of human being and other animals are believed to be efficiently operating for locomotion and other motions, suitably utilizing the physical laws applicable to legs and arms. If robots can utilize such physical laws, they can be made to walk and do other motions with a high energy conversion efficiency without requiring detailed models (and hence with a small load for computations) and drive torques for actuators.
Recently, non-model-based approaches that do not require information on detailed mechanical models and environments in advance are attracting attention in view of the above-identified problems of the model-based control technique. A typical example of such a control technique is the motion control of a robot using a model of neural oscillators that are believed to be inherent in the nervous systems of living creatures.
Neural oscillators are formulated by Matsuoka (see K. Matsuoka: “Sustained oscillator generated by mutually inhibiting neurons with adaptation”, Biological Cybernetics, 52, pp. 345-353 (1985)). Particularly, it is known that a neural oscillator (coupled oscillator) obtained by mutual inhibitory connection of two neural elements shows self-oscillation with a natural frequency that is determined by the parameters of the neural elements. It is also known that such a neural oscillator can input signals from the outside and shows “an entrainment phenomenon” of outputting a signal in response to a periodic input with a frequency close to the natural frequency of the neural oscillator with a predetermined phase delay.
A neural oscillator can output an adaptively synchronized signal in response to an appropriate input (feedback) from the environment because of the feature of self-oscillation and entrainment characteristics it has. Therefore, it is possible to form a controller that is robust against environmental changes by means of neural oscillators. For example, it is possible to control the walking motion of a robot by taking the movable sections, or at least a par thereof, for oscillators having such entrainment characteristics, handling the walking motion and other motions of the robot as periodic motion and determining or controlling the phase and the frequency of each of the oscillators. Then, continuation of such a periodic motion can be regarded as “stable locomotion”.
To date, examples of application of neural oscillators to biped locomotion (see G. Taga, Y. Yamaguchi, H. Shimizu: “Self-organized control of bipedal locomotion by a neural oscillators in unpredictable environment”, Biological Cybernetics, vol. 65, pp. 147-159 (1991); G. Taga, “Dynamic Design of Brain and Body—Non-Linear Dynamics of Perception and Development”, Kaneko Shobo (2002); Hase, Yamazaki, “Generation of a Motion Resembling Real Biped Locomotion using neural oscillator and genetic algorithm”, Papers of the Society of Instrument and Control Engineers, Vol. 33, No. 5, pp. 448-454 (1997); Nakamura, Sato, Ishii, “Reinforcement Learning for Rhythmic Movements Using a Neural Oscillator Network”, Papers of the Institute of Electronics Information and Communication Engineers, Vol. J87-D-II, No. 3, pp. 893-902 (2004); and G. Endo, J. Nakanishi, J. Morimoto, G. Chen, “Experimental Study of a Neural Oscillator for Biped Locomotion Using QRIO”, International Conference on Robotics and Automation, pp. 598-604 (2005)), those to quadruped locomotion (see Fukuoka, Kimura, “Biologically Inspired Adaptive Dynamic Walking of a Quadruped Robot Irregular Terrain—Adjustment Based on Somatic Sensation and Vestibular Sensation—”), those to periodic motions of arms (see Matthew M. Williamson, “Robot Arm Control Exploiting Natural Dynamics”, Massachusetts Institute of Technology, Ph-D Theses (1999)) and those to juggling motions (see S. Miyakoshi, M. Yamakita, K. Furuta, “Juggling Control Using Neural Oscillator”, International Conference on Intelligent Robots and Systems (IROS '94), Vol. 2, pp. 1186-1193 (1994)).
However, the above cited examples of application mostly handle steady periodic motions and periodic oscillations are used for entrainment. In other words, the feedback signal input to a neural oscillator from the environment is a periodic signal and there is no example that uses a non-periodic signal such as a pulse wave as feedback signal. Thus, no satisfactory study has been made so far on the applicability of neural oscillators to non-periodic motions such as a motion for getting to a goal. Additionally, the amplitude of the feedback signal is about a half of that of the neural oscillator at most and hence it is not possible to produce a large entrainment region by a periodic signal.
An abrupt external turbulence can occur when a locomotive robot walks on an unleveled ground or at high speed or encounters an obstacle. Then, if the attitude of the robot goes out of the entrainment region of its neural oscillators, it is difficult for the robot to recover the original attitude by a feedback signal that is a periodic signal.